Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Oracle estimators for the benchmarking of source separation algorithms
Signal Processing
Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Complex nonconvex lp norm minimization for underdetermined source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A general modular framework for audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Double sparsity: towards blind estimation of multiple channels
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
| Hi-index | 35.68 |
We propose a method to count and estimate the mixing directions in an underdetermined multichannel mixture.The approach is based on the hypothesis that in the neighborhood of some time-frequency points, only one source essentially contributes to the mixture: such time-frequency points can provide robust local estimates of the corresponding source direction.At the core of our contribution is a statistical model to exploit a local confidence measure, which detects the time-frequency regions where such robust information is available. A clustering algorithm called DEMIX is proposed to merge the information from all time-frequency regions according to their confidence level. So as to estimate the delays of anechoic mixtures and overcome the intrinsic ambiguities of phase unwrapping as met with DUET, we propose a technique similar to GCC-PHAT that is able to estimate delays that can largely exceed one sample. We propose an extensive experimental study that shows the resulting method is more robust in conditions where all DUET-like comparable methods fail, that is, in particular, a) when time-delays largely exceed one sample and b) when the source directions are very close.